[leftmargin=*]
Re-training: Train a binary SVM given the currently selected
proposals from the positive images and the proposals in negative images.

Re-localization: Given the current SVM select the proposal with
the highest score from each positive image.
In [1]},
the re-localization is guided by a class-agnostic objectness measure to guide
the selection toward objects.
Therefore, the selection for a positive image
\(\mathbf {I}\)  with bounding box proposals \(B\)  is updated as
\(b^* = \operatornamewithlimits{arg\,max}_{b\in B}\quad \text{SVM}(\mathbf {I}, b) + \gamma O(\mathbf {I}, b)~,\) 
where \(O\)  is the objectness model and \(\gamma \)  adjusts its importance.

